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Gongfan Fang

Gongfan Fang has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Xinchao Wang, Xinyin Ma.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 13 · 58 works · 1,277 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 391 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3325

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓National University of SingaporeSingapore · Singapore2023–2026 · 30 paperscurrent🎓Zhejiang UniversityHangzhou · China2019–2023 · 13 papers🏢Alibaba GroupHangzhou · China2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 1 workEngineering · 1 workMechanical Engineering · 1 work

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20251 paperGongfan Fang's papers of 202520231 paperGongfan Fang's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. LLM-Pruner: On the Structural Pruning of Large Language Models (2023 · 448 citations)
    Large language models are expensive to deploy, yet most compression methods shrink a model for one downstream task.
  2. CoT-Valve: Length-Compressible Chain-of-Thought Tuning (2025 · 64 citations)
    Reasoning models such as QwQ and DeepSeek-R1 produce long chains of thought even for trivial questions, which wastes tokens.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. LLM-Pruner: On the Structural Pruning of Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Xinyin Ma, Gongfan Fang, Xinchao Wang2023 · 448 citations
  2. CoT-Valve: Length-Compressible Chain-of-Thought TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Xinyin Ma, Guangnian Wan, Runpeng Yu, et al.2025 · 64 citations
  3. Efficient Reasoning Models: A Survey ↗arXiv:2504.10903 · no summary yetSicheng Feng et al.2025 · 37 citations

 Co-authors here

2 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 391 times (of the 141,840 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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